UI-TARS 7B vs Nemotron 3.5 Super VL
UI-TARS 7B costs 2.5x less per output token than Nemotron 3.5 Super VL ($0.20 vs $0.50 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
- Coding
- No data
- Overall
- 35.6
- Output $/M
- $0.50
UI-TARS 7B or Nemotron 3.5 Super VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick UI-TARS 7B for output tokens 2.5x cheaper ($0.20 vs $0.50 per million).
No benchmark has tested both yet. UI-TARS 7B has results on Kagi LLM Benchmark; Nemotron 3.5 Super VL on LiveBench coding, SciCode and AA Intelligence Index. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Nemotron 3.5 Super VL has a AA Intelligence Index result, 26.5.
Directional only
Web design
Design Arena: websites
No call
Only Nemotron 3.5 Super VL has a Design Arena: websites result, 1172.
Directional only
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
UI-TARS 7B
UI-TARS 7B $0.0002, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- UI-TARS 7B not benchmarked, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- UI-TARS 7B $0.20 against Nemotron 3.5 Super VL $0.50 per million output tokens, 2.5x apart.
- Context window
- UI-TARS 7B takes 128K tokens, Nemotron 3.5 Super VL 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
| Benchmark | UI-TARS 7B | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | UI-TARS 7B | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 26.5 | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | UI-TARS 7B | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 70.9% | Only Nemotron 3.5 Super VL measured |
| LiveBench browser | Not measured | 4.0% | Only Nemotron 3.5 Super VL measured |
| LiveBench long agentic | Not measured | 15.8% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | UI-TARS 7B | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 30.7% | Only Nemotron 3.5 Super VL measured |
| Kagi LLM Benchmark | 20.7% | Not measured | Only UI-TARS 7B measured |
| Neither model has a result on the other 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | UI-TARS 7B | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1172 | Only Nemotron 3.5 Super VL measured |
| Design Arena: UI components | Not measured | 1148 | Only Nemotron 3.5 Super VL measured |
| Design Arena: game dev | Not measured | 1110 | Only Nemotron 3.5 Super VL measured |
| Design Arena: 3D | Not measured | 1127 | Only Nemotron 3.5 Super VL measured |
| Design Arena: all code categories | Not measured | 1154 | Only Nemotron 3.5 Super VL measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | UI-TARS 7B | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| Long context reasoning (AA-LCR) | Not measured | 69.0% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 46 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
UI-TARS 7B costs less
Repository review
50K input + 3K output tokens
UI-TARS 7B costs less
Agent loop
200K input + 10K output tokens
UI-TARS 7B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | ByteDance UI-TARS 7B | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | ByteDance | NVIDIA |
| Weights | Open weights | Open weights |
| Released | July 22, 2025 | October 15, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $0.10 | $0.15 |
| Output $/Mtok | $0.20 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is UI-TARS 7B better than Nemotron 3.5 Super VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick UI-TARS 7B for output tokens 2.5x cheaper ($0.20 vs $0.50 per million).
Which is cheaper, UI-TARS 7B or Nemotron 3.5 Super VL?
UI-TARS 7B costs $0.10 per million input tokens and $0.20 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, UI-TARS 7B or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 128K.
Has any benchmark tested both UI-TARS 7B and Nemotron 3.5 Super VL?
No benchmark has tested both yet. UI-TARS 7B has results on Kagi LLM Benchmark; Nemotron 3.5 Super VL on LiveBench coding, SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 23, 2026.